Leveraging Uncertainty in Deep Learning for Pancreatic Adenocarcinoma Grading

TL;DR

This paper introduces Bayesian CNNs for pancreatic cancer grading from histology images, emphasizing uncertainty estimation to improve prediction reliability and clinical trust.

Contribution

It presents a novel application of Bayesian CNNs for pancreatic cancer grading, integrating uncertainty quantification to enhance accuracy and decision-making.

Findings

Uncertainty correlates with prediction error.

Uncertainty helps set effective acceptance thresholds.

Method improves reliability in clinical settings.

Abstract

Pancreatic cancers have one of the worst prognoses compared to other cancers, as they are diagnosed when cancer has progressed towards its latter stages. The current manual histological grading for diagnosing pancreatic adenocarcinomas is time-consuming and often results in misdiagnosis. In digital pathology, AI-based cancer grading must be extremely accurate in prediction and uncertainty quantification to improve reliability and explainability and are essential for gaining clinicians trust in the technology. We present Bayesian Convolutional Neural Networks for automated pancreatic cancer grading from MGG and HE stained images to estimate uncertainty in model prediction. We show that the estimated uncertainty correlates with prediction error. Specifically, it is useful in setting the acceptance threshold using a metric that weighs classification accuracy-reject trade-off and misclassification cost controlled by hyperparameters and can be employed in clinical settings.